code: def parse_tweets_set(
      filename,
      label,
      word_tokenizer=None,
      sent_tokenizer=None,
      skip_header=True,
  ):
      """
      Parse csv file containing tweets and output data a list of (text, label) tuples.
  
      :param filename: the input csv filename.
      :param label: the label to be appended to each tweet contained in the csv file.
      :param word_tokenizer: the tokenizer instance that will be used to tokenize
          each sentence into tokens (e.g. WordPunctTokenizer() or BlanklineTokenizer()).
          If no word_tokenizer is specified, tweets will not be tokenized.
      :param sent_tokenizer: the tokenizer that will be used to split each tweet into
          sentences.
      :param skip_header: if True, skip the first line of the csv file (which usually
          contains headers).
  
      :return: a list of (text, label) tuples.
      """
      tweets = []
      if not sent_tokenizer:
          sent_tokenizer = load("tokenizers/punkt/english.pickle")
  
      with codecs.open(filename, "rt") as csvfile:
          reader = csv.reader(csvfile)
          if skip_header == True:
              next(reader, None)  # skip the header
          i = 0
          for tweet_id, text in reader:
              # text = text[1]
              i += 1
              sys.stdout.write(f"Loaded {i} tweets\r")
              # Apply sentence and word tokenizer to text
              if word_tokenizer:
                  tweet = [
                      w
                      for sent in sent_tokenizer.tokenize(text)
                      for w in word_tokenizer.tokenize(sent)
                  ]
              else:
                  tweet = text
              tweets.append((tweet, label))
  
      print(f"Loaded {i} tweets")
      return tweets
  
imports: import codecs
  import csv
  import json
  import pickle
  import random
  import re
  import sys
  import time
  from copy import deepcopy
  import nltk
  from nltk.corpus import CategorizedPlaintextCorpusReader
  from nltk.data import load
  from nltk.tokenize.casual import EMOTICON_RE
label_desc: Use image node_id 9 for calling a global function with example usage: parse_tweets_set(filename, label, word_tokenizer, sent_tokenizer, skip_header) and returns: tweets